In this work, we propose a novel method termed \emph{Frustum ConvNet
(F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region
proposals in an RGB image, our method first generates a sequence of frustums
for each region proposal, and uses the obtained frustums to group local points.
F-ConvNet aggregates point-wise features as frustum-level feature vectors, and
arrays these feature vectors as a feature map for use of its subsequent
component of fully convolutional network (FCN), which spatially fuses
frustum-level features and supports an end-to-end and continuous estimation of
oriented boxes in the 3D space. We also propose component variants of
F-ConvNet, including an FCN variant that extracts multi-resolution frustum
features, and a refined use of F-ConvNet over a reduced 3D space. Careful
ablation studies verify the efficacy of these component variants. F-ConvNet
assumes no prior knowledge of the working 3D environment and is thus
dataset-agnostic. We present experiments on both the indoor SUN-RGBD and
outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD,
and at the time of submission it outperforms all published works on the KITTI
benchmark. Code has been made available at:
{https://github.com/zhixinwang/frustum-convnet.}

本文介绍了一种名为 Frustum ConvNet 的新方法，可以从点云中识别 3D 物体。该方法首先在 RGB 图像中给出 2D 区域的建议，然后为每个区域建立一个锥体序列，并使用获得的锥体组合局部点。Frustum ConvNet 通过锥体级特征向量汇总逐点特征，并将这些特征向量作为特征映射数组用于其后续分量的完全卷积网络（FCN）来空间聚合锥体级特征，支持 3D 空间中定向框的端到端和连续估计。